縱向胸部X光報告的感知轉變N中最佳取樣

摘要

在縱向臨床實務中，每張胸部X光片都是在參照病人先前檢查的情況下判讀，而放射科醫師報告的重點多半是從一次就診到下次就診的變化。據我們所知，我們提出了首個無需訓練的最佳N項取樣方案，用於預訓練的胸部X光報告生成器，該方案明確感知從縱向先前到當前的變化。我們稱之為「變化感知最佳N項取樣」：每份報告被拆分成句子並嵌入為R^d中的無序集合；每個（先前，當前）配對通過一個集合到集合的距離轉化為固定維度的方向向量，該距離設計用於編碼兩個集合之間的變化；候選者透過其候選變化向量與緩存的真實訓練變化向量庫之間的餘弦距離進行評分，並以最小值或kNN方式聚合。我們用四種方向性集合距離（均值移位、新穎殘差、有向離散豪斯多夫錨點、以及加權成本最優傳輸）實例化該框架，並在一個多次就診的AP-PA隊列上進行評估，在三種提示下對三個視覺語言生成器進行推理。變化感知最佳N項取樣全面優於隨機選取，其中「印象」章節的相對提升最大。

English

In longitudinal clinical practice, every chest X-ray is read in the context of the patients prior exam, and much of what the radiologist communicates is the change from one visit to the next. To the best of our knowledge, we present the first training-free best-of-N sampling scheme for pre-trained chest X-ray report generators that is explicitly aware of this longitudinal prior to current transition. We call it transition-aware best-of-N sampling, each report is split into sentences and embedded into an unordered set in Rd; each (prior, current) pair is reduced to a fixed-dim directional vector via a set-to-set distance designed to encode the change between the two sets; and candidates are scored by cosine distance from their candidate transition vector to a cached bank of ground-truth training transition vectors, aggregated as min or kNN. We instantiate the framework with four directional set distances (mean-shift, novelty residual, directed-Hausdorff anchor, and cost-weighted optimal transport) and evaluate on a multi-visit AP-PA cohort, running inference under three prompts on three vision-language generators. Transition-aware best-of-N outperforms random selection across the board, with the largest relative gains on the Impression section.